lda-loop-n-topics.py

perplexities = []
coherence = []
num_topics = [3,4, 10, 20] + list(range(5, 75, 10))
for nt in tqdm(num_topics):
    lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                           id2word=words,
                                           num_topics=nt, 
                                           random_state=2,
                                           update_every=1,
                                           passes=10,
                                           alpha='auto',
                                           per_word_topics=True)
    perplexities.append(lda_model.log_perplexity(corpus))
    coherence_lda_model = CoherenceModel(model=lda_model, texts=docs, dictionary=words, coherence='c_v')
    print(f"Num: {nt} - Per: {lda_model.log_perplexity(corpus)} - Coh: {coherence_lda_model.get_coherence()}")
    coherence.append(coherence_lda_model.get_coherence())